Glass Blowers, Molders, Benders, and Finishers
51-9195.04Median wage $46,170/yr33,190 employed (US)Rank #677 of 923 scored · top 73% by substitution
Shape molten glass according to patterns.
Sub-scores
0–100 · band = confidence interval from rater disagreement
Substitution20
Exposure11
Augmentation21
Substitution — the headline: capability discounted by cost, barriers and adoption.
Exposure — technical capability alone, regardless of whether anyone deploys it.
Augmentation — how much AI assists without replacing. High here + moderate substitution = a changing job, not a disappearing one.
Tasks on the substitution scale
16 rated tasks, binned by substitution score.
Position among all scored occupations
Distribution of 923 occupation scores; the marker is this occupation.
Tasks with substitution ≥ 70
6%
Run 1.0.0-draft.1 · computed 2026-08-05 · rater panel: claude-sonnet-5, claude-haiku-4-5-20251001 · intervals span rater disagreement.
Why this score
The five weighted dimensions of the composite, averaged across this occupation's tasks (importance-weighted, panel mean). Exact weights and formulas: /api/v1/methodology.
Task automatabilityw 35%13
panel mean rating 1.5/5 → substitution pressure 13/100
Technical feasibility todayw 20%8
panel mean rating 1.3/5 → substitution pressure 8/100
Cost vs. human wagew 15%13
panel mean rating 1.5/5 → substitution pressure 13/100
Adoption barriersw 20%inverted — strong barriers lower the score56
panel mean rating 2.8/5 (barrier strength) → substitution pressure 56/100
Sector adoption velocityw 10%5
panel mean rating 1.2/5 → substitution pressure 5/100
Task breakdown (16 tasks)
Substitution pressure per task, weighted by O*NET importance in the composite. Expand a task for the full rater audit trail — every rating, every model, every rationale.
Record manufacturing information, such as quantities, sizes, or types of goods produced.
71CI 65–77 · exposure 70 · augmentation 75 · importance 4.2/5 · click for rater detail
Record manufacturing information, such as quantities, sizes, or types of goods produced.
71| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 3/5 | Manufacturing is moderately digitizing, with vision systems and IoT sensors becoming more common, but full automation of production logging is not yet ubiquitous—pilots and partial rollouts are more typical than industry-wide deployment. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Glass manufacturing is a traditional, often small-scale, physically-oriented industry with slower digitization and automation adoption compared to information-sector work. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI-assisted data capture (autocomplete, validated computer vision suggestions, automatic unit conversion) significantly raises worker productivity for manual logging, while the worker retains quality control and exception handling. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI-enabled tracking tools and digital forms significantly speed up and reduce errors in recording manufacturing data, letting workers focus more on production rather than paperwork. |
| Task automatability | claude-haiku-4-5-20251001 | 4/5 | Recording manufacturing information like quantities, sizes, and types is largely data entry and logging, which AI systems can automate nearly end-to-end from production monitoring systems, digital forms, or computer vision inspection—achieving >50% time savings. Some context-dependent judgments about size classification or defect categorization may require human review, preventing a perfect 5. |
| Task automatability | claude-sonnet-5 | 4/5 | Recording production data (quantities, sizes, types) is a structured data-entry task that can largely be automated via barcode/sensor scanning, MES systems, or voice-to-text logging feeding into databases with minimal human input. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | There are no legal or licensing barriers to automating data recording in manufacturing. The primary friction is organizational inertia and quality verification protocols, which are light compared to regulated industries. |
| Adoption barriers | claude-sonnet-5 | 1/5 | There are no licensing, legal, or safety barriers to automating simple production record-keeping; it's a routine clerical/administrative sub-task. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 4/5 | AI-based vision and logging systems have dropped significantly in cost and run continuously, typically well below the hourly wage of a dedicated recording employee, especially at volume. Integration and oversight add cost but remain substantially cheaper than full human labor. |
| Cost vs. human wage | claude-sonnet-5 | 4/5 | Automated data capture (scanners, sensors, software) is far cheaper per unit of data logged than continuous manual record-keeping by a skilled glass worker, though integration costs exist upfront. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 3/5 | Commercial vision systems and manufacturing data collection platforms exist and deploy in factories, but error rates on part classification and size measurement remain material in many glass production settings; full end-to-end automation of recording without human spot-checks is not yet standard practice at scale. |
| Technical feasibility today | claude-sonnet-5 | 4/5 | Manufacturing execution systems (MES) and IoT-enabled production tracking are widely deployed in factories today to automatically log production counts and specs, though some manual entry still occurs on legacy lines. |
Inspect, weigh, and measure products to verify conformance to specifications, using instruments such as micrometers, calipers, magnifiers, or rulers.
29CI 23–35 · exposure 25 · augmentation 38 · importance 4.5/5 · click for rater detail
Inspect, weigh, and measure products to verify conformance to specifications, using instruments such as micrometers, calipers, magnifiers, or rulers.
29| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Glass blowing and finishing is a traditional, craft-oriented sector with smaller production runs and highly specialized, skilled workers. Digitization and automation adoption rates remain low in this domain compared to high-tech manufacturing, and existing firms have little track record of deploying AI inspection systems at scale. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Manufacturing, especially craft/artisan glass work, is a lower-digitization sector with slower AI adoption compared to information or finance sectors, though automated QC is growing in large-scale glass production. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could potentially assist by flagging suspicious measurements or highlighting areas for human review, but the task is fundamentally hands-on measurement and judgment. The augmentation value is limited because human inspectors already perform the measurement efficiently with simple, proven instruments, and adding an AI layer would introduce latency rather than meaningfully amplify human capability. |
| Augmentation potential | claude-sonnet-5 | 3/5 | Digital calipers, computer-vision-assisted measurement tools, and automated data logging can meaningfully speed up and improve accuracy of inspection tasks, even if a human remains needed for judgment and physical handling. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI vision systems can measure and inspect some glass products, the task requires handling delicate glass items, operating precise hand instruments (micrometers, calipers), and making contextual judgments about surface quality and conformance in a physical, three-dimensional manufacturing environment. Current AI cannot reliably perform the full end-to-end inspection workflow or achieve the 50% time-saving threshold at equal quality without substantial human oversight. |
| Task automatability | claude-sonnet-5 | 2/5 | Automated inspection systems (machine vision) can measure and verify glass products against specs, but this requires fixed hardware setup integrated into production lines rather than off-the-shelf general AI, and manual handheld measurement tasks remain common in smaller shops. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Quality conformance inspection in manufacturing is often subject to regulatory oversight (e.g., consumer product safety standards, contractual requirements for traceability), and many jurisdictions require a qualified human to certify that products meet specifications. Liability and safety concerns around defective glass products create strong organizational and legal friction against full automation. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement for this inspection task, though quality control certifications in some industries (e.g., aerospace, medical glass) may impose some documentation standards; otherwise adoption is a matter of operational choice. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | A deployed vision inspection system with hardware, integration, and ongoing oversight would likely cost as much or more than employing a skilled glass inspector, particularly when accounting for retraining, false-positive handling, and the need for human final sign-off on conformance decisions. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Vision-based inspection systems require significant capital investment (cameras, sensors, integration) that may not be justified for smaller-scale or custom glass work, making cost comparable to or higher than manual inspection in many settings. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Computer vision systems exist for industrial quality inspection, but they typically require custom calibration, controlled lighting, and are most effective on flat or standardized geometries. Existing deployed products show material error rates when inspecting complex glass shapes and subtle defects, and do not yet operate reliably at production scale in glass manufacturing without human verification. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Machine vision quality control exists in high-volume glass manufacturing, but for artisanal/finishing work with micrometers and calipers, deployed AI products are narrow and not broadly used across this occupation. |
Operate and maintain finishing machines to grind, drill, sand, bevel, decorate, wash, or polish glass or glass products.
27CI 19–35 · exposure 20 · augmentation 38 · importance 4.0/5 · click for rater detail
Operate and maintain finishing machines to grind, drill, sand, bevel, decorate, wash, or polish glass or glass products.
27| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Glass finishing is performed in small craft and specialty workshops as well as light manufacturing, sectors with low digitization, limited capital for automation, and preference for skilled human artisans. Adoption of AI or robotics in this craft-oriented industry has been minimal. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Glass manufacturing is a physical, moderately digitized sector where robotic/automated finishing exists in large-scale production but adoption is slow and uneven across smaller shops and custom glass work. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could potentially assist with quality inspection via computer vision or suggest machine parameters, but human operators remain essential for safe handling, real-time adjustment, and ensuring consistent finish. The augmentation opportunity is limited because the bottleneck is physical control and adaptive judgment rather than information processing. |
| Augmentation potential | claude-sonnet-5 | 3/5 | Sensors, machine vision for defect detection, and automated control systems can assist workers in monitoring machine performance and catching quality issues, improving efficiency without replacing the operator. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Operating finishing machines requires real-time sensory feedback, physical dexterity, and adaptive decision-making in response to glass properties and defects. Current AI cannot reliably detect quality issues, orient delicate glass safely, or adjust machine parameters dynamically across diverse glass types to achieve consistent finish quality. |
| Task automatability | claude-sonnet-5 | 2/5 | Physical machine operation, material handling, and quality inspection of glass require manipulation and sensory judgment that current general-purpose AI cannot perform end-to-end; only narrow, pre-programmed automation exists for specific steps. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | Safety regulations govern operation of grinding and polishing machines, and there is no legal requirement for human licensure, but occupational safety standards and the need for real-time quality judgment create moderate organizational friction. Worker displacement risk and equipment liability provide some adoption friction. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement blocks automation, but physical workspace integration, safety regulations around machinery, and capital costs create moderate friction to full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The capital cost of specialized robotic systems capable of safe glass handling, combined with integration and ongoing maintenance, remains substantially higher than the loaded wage of a skilled glass finisher. Current solutions require significant custom engineering rather than off-the-shelf automation. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Specialized robotic finishing equipment has high capital and integration costs, and for small-batch or custom glass work the human labor cost is often comparable or cheaper than automation investment. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed production systems exist that autonomously operate multi-step glass finishing machines end-to-end. While robotic arms exist for some industrial tasks, glass finishing's requirement for fine material judgment and continuous parameter adjustment has not been solved at scale in real production environments. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Industrial CNC and robotic finishing systems exist in production for standardized glass products, but they are task-specific hardware/software solutions, not general AI systems, and require significant human setup, monitoring, and adjustment. |
Develop sketches of glass products into blueprint specifications, applying knowledge of glass technology and glass blowing.
26CI 23–30 · exposure 16 · augmentation 50 · importance 3.8/5 · click for rater detail
Develop sketches of glass products into blueprint specifications, applying knowledge of glass technology and glass blowing.
26| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Glass blowing remains a small, craft-oriented, physically-located industry with limited digitization and slow technology adoption. Most glass workshops are small operations without formal CAD pipelines, limiting the sectors and firms where automation of this task would even be attempted. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Glassblowing and craft manufacturing is a small-scale, low-digitization sector with minimal AI tool adoption for this kind of design-to-spec work. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can usefully assist by auto-converting sketches to preliminary CAD drawings and flagging standard design constraints, but the artisan must retain control over aesthetics, glass-specific engineering, and feasibility judgment. This is a real productivity boost for the designer but does not transform the task. |
| Augmentation potential | claude-sonnet-5 | 3/5 | General-purpose AI (CAD assistance, generative design tools, LLMs for documentation) can help draft or refine blueprint text and visualize concepts, aiding the human expert in parts of the task. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can assist with basic technical drawings and CAD conversion, developing blueprint specifications requires deep domain knowledge of glass material properties, thermal behavior, and blowing feasibility that demands iterative human expertise. Current AI systems cannot reliably translate sketches into production-ready specifications that account for glass viscosity, annealing, and structural integrity. |
| Task automatability | claude-sonnet-5 | 2/5 | AI can assist with drafting or visualizing specifications from sketches, but translating a hands-on craft sketch into precise blueprint specs requiring deep material and process knowledge is not reliably end-to-end automatable today. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | Quality control and safety standards in glassware production create some friction—blueprints must satisfy manufacturing tolerances and safety codes—but there is no hard legal requirement that a licensed human must sign off on glass product specifications, only practical organizational validation needs. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement exists, but the specialized tacit knowledge of glass technology and blowing techniques creates practical friction against full substitution. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI-assisted CAD tools and sketch-to-drawing converters are moderately cheap per use, but the necessary human review, correction, and domain validation by a skilled glass technologist means total cost remains high relative to the small time savings achieved. The specialized nature of glass manufacturing keeps human oversight costs substantial. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Given the lack of specialized tools, any AI use would require significant custom setup and human verification, making the all-in cost likely comparable to or higher than a skilled specialist doing it directly. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed product reliably performs the full task of converting hand sketches into detailed glass-specific blueprint specifications with material-appropriate engineering constraints. AI drafting and CAD tools exist but lack the specialized glass technology knowledge and manufacturing validation this task requires. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed production system specifically converts glassblowing sketches into technical blueprint specifications; this remains a niche, expert-driven task with no commercial AI product addressing it. |
Determine types and quantities of glass required to fabricate products.
25CI 23–28 · exposure 20 · augmentation 38 · importance 4.2/5 · click for rater detail
Determine types and quantities of glass required to fabricate products.
25| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Glass blowing and molding remain small-batch, artisanal manufacturing with low overall digitization; adoption of AI for material planning lags far behind information-sector adoption rates. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Glass blowing and molding is a low-digitization, physical craft trade with minimal AI adoption reported in production settings. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI could usefully assist by suggesting glass types and quantities based on product parameters, helping artisans check calculations and cross-reference material properties, while the human retains final specification authority. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could assist with calculations or inventory lookups for glass quantities, but the core physical and craft judgment aspects limit meaningful augmentation. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | This task requires translating product specifications into material requirements, which involves some calculation and material-property matching that AI could assist with, but current systems struggle with the contextual judgment around product design constraints, manufacturing tolerances, and glass type selection that experienced craftspeople apply. |
| Task automatability | claude-sonnet-5 | 2/5 | This requires physical assessment of glass stock, knowledge of specific product specs, and often tacit craft judgment about material behavior during forming, which current AI cannot directly perceive or decide end-to-end.' |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | Glass selection and material procurement involve some supply-chain and quality-control constraints, but are not strictly regulated or licensed; however, craft knowledge and supplier relationships create moderate organizational friction to full automation. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing requirement specifically, but organizational friction and reliance on specialized craft expertise and physical inspection create moderate barriers to substitution. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Integration of AI material-estimation systems into glass fabrication workflows would require custom setup and oversight; the loaded cost per task instance would be comparable to or exceed a skilled worker's time doing specification review and ordering. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Without mature deployed systems, any AI solution would require custom integration and human oversight, making costs comparable to or higher than simply having a skilled worker do it. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | While material estimation software and CAD systems exist, no deployed AI product reliably performs glass type and quantity determination end-to-end for diverse glassblowing products without significant human override and domain expertise. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product performs material quantity/type determination for glass fabrication in production settings; this remains a specialized craft judgment task. |
Heat glass to pliable stage, using gas flames or ovens and rotating glass to heat it uniformly.
21CI 10–33 · exposure 13 · augmentation 13 · importance 4.5/5 · click for rater detail
Heat glass to pliable stage, using gas flames or ovens and rotating glass to heat it uniformly.
21| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Glass blowing is a traditional craft practiced largely by small, artisanal, or specialized studios with low digitization. Adoption of AI-driven automation is minimal; the sector remains labor-intensive and resistant to capital-heavy machinery that would displace skilled workers. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Glassblowing and glass manufacturing craft work is a low-digitization, physical trade sector showing minimal AI adoption; existing automation is traditional machinery, not AI-driven. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could potentially provide real-time feedback on glass temperature via thermal imaging or material-state prediction, but such augmentation tools are not widely available or integrated into glass-blowing workflows. Most enhancement would require custom development and would offer modest productivity gains over existing practice. |
| Augmentation potential | claude-sonnet-5 | 1/5 | AI offers no meaningful assistance for the physical act of heating and rotating glass; this is a hands-on sensory-motor task outside current AI's scope of assistance. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While heating glass is a physical process that could theoretically be automated with furnaces and robotic arms, the task requires real-time sensory assessment of glass pliability—detecting the precise moment when glass reaches the correct state through visual and sometimes tactile feedback. Current AI-integrated robotic systems lack the reliable material-state sensing and adaptive control needed to consistently match human judgment without significant human oversight. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical manipulation task requiring precise manual control of glass in a furnace, tracking viscosity and heat distribution by feel and sight; no current AI system can perform this physical operation. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | Worker safety regulations and quality standards create some friction, and small artisanal glass shops lack the scale and capital to adopt automation. However, there is no legal requirement that a licensed human must perform the heating itself, so regulatory barriers are moderate rather than hard. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement exists for this specific task, but the physical/tactile nature of glassblowing and safety considerations around molten glass create practical barriers to any automated substitution. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Custom robotic arms with heating control and vision systems are capital-intensive and require significant integration costs, often exceeding the loaded wage of a skilled glass blower over several years. The ROI is poor for tasks that demand high per-piece variation and frequent recalibration. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI-driven substitute; existing automation for glass forming is mechanical/robotic, not AI-based, so comparing AI inference cost to human wage is not meaningful and any AI-based approach would be more costly. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Industrial glass-heating furnaces exist, but they are designed for batch or continuous processes rather than adaptive, task-specific heating to a particular pliability point. Robotic glass handling systems are rare in production and typically require human operators to verify material state; no deployed product reliably performs this specific task end-to-end without human intervention. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI/robotic product performs glass heating and rotation at production scale; this remains a skilled manual craft or industrial process controlled by fixed automation, not AI. |
Cut lengths of tubing to specified sizes, using files or cutting wheels.
17CI 10–24 · exposure 8 · augmentation 13 · importance 3.7/5 · click for rater detail
Cut lengths of tubing to specified sizes, using files or cutting wheels.
17| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Glass working remains a largely artisanal, small-batch industry with low digitization and automation adoption rates. Shops typically rely on skilled manual craftspeople, and capital investment in specialized automation is economically marginal for most firms. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Glassblowing and related craft manufacturing is a low-digitization, physically dexterous trade sector where AI adoption is minimal and slow. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could assist by automating measurement and cut-point identification through vision systems, but the actual cutting operation requires human judgment about glass integrity, tool wear, and quality feedback that would remain with the operator. |
| Augmentation potential | claude-sonnet-5 | 1/5 | AI offers essentially no assistance to the physical act of cutting glass tubing with files or cutting wheels. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Cutting glass tubing to specified sizes requires precise measurement and controlled manual application of force, which current AI systems cannot reliably perform end-to-end. While vision systems can measure tubing and identify cut points, the physical execution and quality control remain heavily dependent on skilled manual operation and real-time tactile feedback. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical manual task requiring hand-eye coordination and fine motor skill with glass tubing; no AI system can perform the physical cutting action itself. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | While there are no strict licensing barriers to automating this task, adoption is hindered by the specialized nature of glass work, high setup costs for equipment, and the craft-focused organizational structure of most glass shops where human expertise is valued. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement exists, but physical dexterity, workshop equipment, and material handling create practical barriers to any automated substitution beyond dedicated CNC-style machinery, not general AI. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Custom robotic systems capable of cutting glass tubing with the required precision would cost substantially more than the labor of a skilled glass blower, particularly for small-batch or custom work typical in the industry. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI-driven substitute for the physical act of cutting glass tubing, so any AI cost comparison is moot—human labor or specialized robotics/machinery would be required instead. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI products reliably perform glass tube cutting independently. This task requires specialized robotic hardware with precision cutting tools and real-time quality assurance, which is not a standard commercial offering for glass-working applications. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product performs manual glass tube cutting; this remains a purely physical craft skill performed by humans with hand tools. |
Place glass into dies or molds of presses and control presses to form products, such as glassware components or optical blanks.
16CI 10–23 · exposure 8 · augmentation 13 · importance 4.7/5 · click for rater detail
Place glass into dies or molds of presses and control presses to form products, such as glassware components or optical blanks.
16| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Glass blowing and molding remain concentrated in small craft-oriented and niche manufacturing firms with low digital infrastructure and capital constraints. These sectors show slow, limited AI adoption overall; the bespoke nature of the work further limits standardization. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Glass manufacturing is a low-digitization, physical, small-scale craft/industrial sector with minimal AI or robotic adoption reported for this specific task. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could assist with minor tasks like monitoring mold temperature or predicting press timing, but the core manual skill—positioning fragile glass and controlling pressure in real time—depends on human sensorimotor feedback and tacit knowledge that AI augmentation currently does not materially enhance. |
| Augmentation potential | claude-sonnet-5 | 1/5 | Current AI offers essentially no meaningful real-time assistance to a human physically placing glass and operating a press in this manual task. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While some elements (mold placement, press control) could be partially automated with specialized equipment, the task requires handling fragile, hot glass with precise timing and spatial judgment. Current AI lacks reliable robotic manipulation of delicate materials in unstructured thermal environments, making end-to-end ≥50% time savings infeasible. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical manipulation task requiring precise handling of molten/hot glass and machine control that current AI systems cannot perform; it requires robotic embodiment far beyond deployed capability.rgb |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | This task has moderate adoption friction: worker safety regulations (heat exposure, injury prevention) and product quality standards create some licensing and liability concerns, but no strict legal requirement for human oversight prevents attempted automation. Customer expectations for artisanal quality also create organizational resistance. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing requirement, but the physical hazards (molten glass, heat, precision timing) and quality-control needs create practical barriers to full automation without specialized custom engineering. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Custom robotic systems for glass handling are capital-intensive and require ongoing maintenance and customization. For a task with variable product geometries and thermal conditions, the all-in cost (hardware, integration, oversight) would typically exceed what skilled glass workers command, making AI substitution economically unfavorable. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI-driven alternative to compare cost against; specialized robotics for this niche hot-glass task would be far more expensive than human labor given low volumes and customization. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed commercial systems today reliably perform this task. The combination of thermal hazards, material fragility, and need for real-time feedback and adjustment remains beyond deployed automation capabilities; only narrow research prototypes exist. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI/robotic product performs glass placement into dies and press control for glassware or optical blank forming at production scale today. |
Spray or swab molds with oil solutions to prevent adhesion of glass.
15CI 15–15 · exposure 0 · augmentation 0 · importance 4.6/5 · click for rater detail
Spray or swab molds with oil solutions to prevent adhesion of glass.
15| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Glass manufacturing remains a craft-heavy, physically intensive industry with limited digital transformation and slow automation adoption outside of large industrial settings. Small to medium glass shops predominate and show minimal AI or advanced robotic adoption. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Glass manufacturing is a low-digitization, physical-labor-intensive sector with minimal AI/robotics adoption for such micro-tasks; automation here would come from industrial robotics investment, not generative AI adoption trends. |
| Augmentation potential | claude-haiku-4-5-20251001 | 1/5 | There is no meaningful way current AI systems assist a human in applying oil solutions to molds; the task is inherently manual and does not benefit from AI advisory or predictive input in practice. |
| Augmentation potential | claude-sonnet-5 | 1/5 | Current AI systems (chatbots, vision models) offer no meaningful assistance to a worker physically spraying or swabbing molds; this is a manual craft task outside AI's functional scope. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires physical manipulation of molds in a production environment, precision application of fluids to irregular surfaces, and real-time judgment about coverage and adhesion prevention. Current AI systems cannot perform end-to-end physical manipulation at the speed and quality required for glass production. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical manipulation task requiring a human or robot to apply oil solutions to molds in a hot, hands-on manufacturing environment; no off-the-shelf AI system performs this physical action.atability today. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | Physical safety requirements and workplace environment standards apply, but there are no licensing requirements or legal mandates that a human must perform this task. Organizational friction around retooling production lines is moderate. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing or legal requirement mandates a human perform this, but physical workplace integration, heat/safety conditions, and capital investment in robotics create practical friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Custom robotics to perform mold oiling, including vision systems, fluid delivery mechanisms, and integration into a glass-working facility, would substantially exceed the loaded cost of a glass-working technician performing this task manually. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI-only solution for this physical task; any automation would require dedicated industrial robotics with significant capital cost, likely exceeding simple human labor cost in most glass shops. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed product reliably performs robotic mold oiling in glass manufacturing at production scale. While robotic arms exist, the specific challenge of applying oil solutions to prevent adhesion of glass—requiring tactile feedback and adaptive coverage decisions—lacks a mature, production-proven solution. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product performs mold spraying/swabbing; this requires robotic actuation and physical dexterity, which is not what current 'AI' products (LLMs, vision systems) provide as a service. |
Shape, bend, or join sections of glass, using paddles, pressing and flattening hand tools, or cork.
15CI 15–15 · exposure 0 · augmentation 0 · importance 4.2/5 · click for rater detail
Shape, bend, or join sections of glass, using paddles, pressing and flattening hand tools, or cork.
15| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | The glass-blowing and molding sector is a traditional craft industry with low digitization, small firm prevalence, and deep human-skill dependencies; AI adoption in this space is negligible and unlikely to accelerate rapidly. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Glass manufacturing and craft trades are low-digitization, physical-labor sectors with minimal AI agent adoption; automation here historically comes from mechanical/robotic systems, not AI software, and adoption is slow. |
| Augmentation potential | claude-haiku-4-5-20251001 | 1/5 | Current AI offers no meaningful assistance to a glass blower or molder performing hand-shaping tasks; the work is fundamentally manual and craft-based, with no obvious role for digital augmentation in real-time material manipulation. |
| Augmentation potential | claude-sonnet-5 | 1/5 | Current AI offers no meaningful real-time assistance to a glassblower physically shaping and bending glass with paddles and hand tools. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires real-time manipulation of molten glass with hand tools (paddles, pressing tools, cork) in a three-dimensional space, demanding continuous sensorimotor feedback and adaptation to material properties that change moment-to-moment. Current AI systems lack the dexterous robotic hardware and real-time tactile sensing to replicate this specialized craft work. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical craft task requiring manual dexterity, real-time tactile feedback with molten glass, and fine motor control that no current AI system (software or robotic) can replicate end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | While not legally licensed, this task involves safety hazards (extreme heat, molten material) and requires tacit craft knowledge that create some organizational and training friction, though barriers are not insurmountable from a regulatory perspective. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing or legal barrier prevents automation of this task, but the physical nature of hot-glass manipulation and need for specialized fixed machinery creates practical friction rather than regulatory barriers. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Robotic systems capable of manipulating hot glass with the precision and flexibility required would be extremely expensive to develop and maintain, likely far exceeding the wage cost of skilled glass workers who perform this task. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI-driven alternative for this specific hand-tool shaping process, so any hypothetical robotic solution would require expensive custom engineering far exceeding human labor costs. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed commercial product reliably performs hot-glass shaping, bending, or joining at production quality today. Specialized industrial glass-forming equipment exists, but it is purpose-built for specific geometric tasks, not general-purpose hand-tool-based glass manipulation as described. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | There are no deployed products that shape or bend molten glass using hand tools autonomously; this remains firmly in the domain of skilled human artisans and specialized fixed automation, not general AI. |
Set up and adjust machine press stroke lengths and pressures and regulate oven temperatures, according to glass types to be processed.
13CI 5–21 · exposure 0 · augmentation 38 · importance 4.2/5 · click for rater detail
Set up and adjust machine press stroke lengths and pressures and regulate oven temperatures, according to glass types to be processed.
13| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Glass manufacturing is a traditional, capital-intensive sector with slower digitization. While some facilities use automated press systems, real-time adjustment and temperature regulation remain heavily manual operations without widespread AI adoption. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Glass manufacturing is a low-digitization, physical, traditional manufacturing sector with slow AI adoption; automation here tends to be industrial control systems rather than AI-driven agents. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could provide limited assistance through temperature monitoring dashboards or pressure recommendations, but the core task of physically setting and adjusting equipment leaves little room for AI to transform worker productivity while they remain in the loop. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI-enabled sensors and predictive analytics can help operators monitor temperature trends and suggest press adjustments, offering moderate assistance while the human still performs the physical setup. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires direct physical interaction with industrial equipment (press stroke adjustment, pressure regulation, oven temperature control) and real-time responsiveness to glass material properties. Current AI systems cannot physically manipulate machinery or operate in production environments without human oversight. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical machine setup and calibration task requiring hands-on adjustment of press strokes, pressures, and oven temperatures based on tactile/visual assessment of glass properties; no current AI system can perform this physical manipulation end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Industrial machinery operation typically requires licensed operators and direct safety responsibility; OSHA regulations and worker safety standards create legal requirements for human authorization and physical presence during equipment adjustments. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing barrier exists, but organizational and safety friction around production line changes and specialized craft knowledge create moderate resistance to full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | AI vision and control systems capable of reliably performing this task would require specialized sensors, hardware integration, and continuous oversight, making total cost substantially higher than a trained glass worker's labor. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | While sensor/PLC-based control systems can partially regulate ovens and presses, the human judgment and physical adjustment components mean AI does not yet substitute the human at a fraction of the cost for the full task. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI product independently sets up and adjusts press machinery or regulates oven temperatures in glass production. This requires integrated control of multiple physical systems and material-specific expertise that exceeds current automation capabilities. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product performs physical machine adjustment and thermal regulation for glass processing; this remains a manual, skilled trade activity with only isolated sensor-based monitoring systems in some plants. |
Superimpose bent tubing on asbestos patterns to ensure accuracy.
10CI 5–15 · exposure 0 · augmentation 0 · importance 3.7/5 · click for rater detail
Superimpose bent tubing on asbestos patterns to ensure accuracy.
10| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Glassblowing is a traditional craft with low digitization, small specialized firms, and physical task requirements. Sector adoption of automation remains minimal, and this particular task sits at the core of artisanal glasswork. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Glass blowing and molding is a low-digitization, small-scale manufacturing trade with minimal AI or robotics adoption reported. |
| Augmentation potential | claude-haiku-4-5-20251001 | 1/5 | Current AI offers no meaningful assistance for real-time physical alignment of hot tubing on patterns; the task requires immediate sensory feedback and manual control that cannot be augmented by existing AI tools. |
| Augmentation potential | claude-sonnet-5 | 1/5 | Current AI tools offer no meaningful assistance for this hands-on physical pattern-matching and shaping task. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires precise physical manipulation of hot glass tubing aligned against patterns in a real-world workspace. Current AI systems cannot perform end-to-end physical manipulation, spatial alignment, and tactile feedback at the speed and accuracy needed for glassblowing work. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a manual physical craft task requiring precise hand-eye coordination and dexterity to align glass tubing against a pattern; no current AI system can perform this physical manipulation. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | This task requires significant human expertise, spatial judgment, and real-time physical adaptation in a hazardous environment with extreme temperatures and asbestos exposure—factors that create both legal liability and practical necessity for a skilled, present human operator. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement exists, but the physical dexterity and craft skill needed create practical (not regulatory) barriers to automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Specialized robotic systems capable of handling glass at temperature would be extremely expensive to acquire, maintain, and reprogram for varying patterns, making their cost far exceed the loaded wage of a skilled glass worker. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute for this physical task, so any AI-based approach would require expensive custom robotics far exceeding the cost of a skilled worker. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed product performs this task reliably in production. The combination of physical dexterity, real-time visual-spatial judgment, and handling of high-temperature materials remains beyond current robotic or AI capabilities in commercial deployment. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI or robotic products perform this specific glass-bending pattern-matching task in production; it remains a manual skilled-trade activity. |
Blow tubing into specified shapes to prevent glass from collapsing, using compressed air or own breath, or blow and rotate gathers in molds or on boards to obtain final shapes.
7CI 5–10 · exposure 0 · augmentation 0 · importance 4.4/5 · click for rater detail
Blow tubing into specified shapes to prevent glass from collapsing, using compressed air or own breath, or blow and rotate gathers in molds or on boards to obtain final shapes.
7| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Glass blowing remains concentrated in small artisanal studios and specialized manufacturing; digitization and AI adoption are minimal in these traditionally hand-craft sectors. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Glassblowing and glass manufacturing craft trades are a low-digitization, physical-labor sector with essentially no AI/robotic adoption for this specific hands-on shaping task. |
| Augmentation potential | claude-haiku-4-5-20251001 | 1/5 | AI offers no meaningful assistance to a glass blower in real-time shaping, as the task requires continuous embodied interaction with hot, dynamic material that AI cannot meaningfully augment. |
| Augmentation potential | claude-sonnet-5 | 1/5 | Current AI offers no meaningful real-time assistance to the physical act of blowing and shaping molten glass; any AI role would be tangential (e.g., design software) rather than augmenting this specific task. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Glass blowing requires real-time sensorimotor control, haptic feedback, and dynamic adjustment to temperature and material properties that current AI cannot perform. The task involves hand-eye coordination, breath control, and tactile judgment that are not automatable with existing systems. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a highly physical, tactile craft skill involving live manipulation of molten glass with precise timing, breath control, and manual dexterity; no AI system can perform this physically at all. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | While no strict licensing requirement applies to the blowing task itself, workplace safety regulations, worker compensation insurance, and the specialized nature of small artisanal glass operations create moderate adoption friction. |
| Adoption barriers | claude-sonnet-5 | 4/5 | While not licensed in a legal sense, the task requires embodied physical skill, real-time sensory feedback, and safety-critical handling of molten materials that create very high practical barriers to automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Specialized robotic glass-forming equipment, if available, would cost hundreds of thousands of dollars with significant setup, training, and maintenance overhead, far exceeding the wage cost of skilled glass blowers. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute, so any hypothetical automation would require expensive custom robotics far exceeding the cost of skilled glassblower labor for this task. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI product performs glass blowing or comparable material-formation tasks autonomously. This is manual craft work requiring embodied skill, dexterity, and environmental sensing beyond the scope of current robotic or AI systems. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI or robotic product performs glassblowing/molding of molten glass at production quality; this remains firmly in human craftsmanship and specialized robotics research at best. |
Design and create glass objects, using blowpipes and artisans' hand tools and equipment.
7CI 5–10 · exposure 0 · augmentation 25 · importance 4.1/5 · click for rater detail
Design and create glass objects, using blowpipes and artisans' hand tools and equipment.
7| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Glass blowing remains a low-digitization, craft-oriented sector with minimal AI adoption. The industry values tradition and hand-skill; automation adoption is extremely slow and limited to routine industrial processes, not bespoke design and creation. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Craft and manufacturing trades like glassblowing show minimal AI/robotics adoption; this is a low-digitization, physically embodied trade with little automation investment. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI offers minimal assistance to the glass-blowing process itself. While generative AI might help with design ideation or sketches beforehand, it cannot assist the live creative execution at the blowpipe, where real-time human artistry and control dominate. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI can assist with design ideation, pattern generation, or inventory/business management, but offers little help with the actual hands-on glassblowing process itself. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires real-time manipulation of molten glass with extreme precision, spatial judgment, and artistic vision—combining motor control, tactile feedback, and creative decision-making that current AI cannot perform end-to-end. No automation exists today that can replicate the hand-eye coordination and adaptive adjustments required in hot glass work. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical craft task requiring manual dexterity, real-time material manipulation with molten glass, and artistic judgment that current AI systems cannot perform robotically at scale. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Strong barriers exist: artisanal glass work is traditionally tied to human craftsmanship and artistic reputation; customers value the handmade/human-created provenance; no regulatory requirement to automate exists, and organizational/cultural attachment to human artisans is high. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing requirement exists, but the physical skill, safety hazards (extreme heat), and artisanal/handmade value proposition create strong practical barriers to automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The specialized equipment, energy costs (maintaining molten glass), and integration complexity far exceed the loaded wage of a skilled glass blower, making automation economically infeasible at current technology. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI system performing this physical task, so any hypothetical robotic solution would be far more expensive than a skilled human artisan today. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed product reliably performs bespoke glass blowing and artistic design autonomously. This remains entirely a domain of skilled human artisans; research into robotic glass manipulation exists but has not achieved production deployment for creative object design. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product exists that can physically blow, shape, and finish glass artifacts; this remains purely a research-stage robotics challenge at best. |
Repair broken scrolls by replacing them with new sections of tubing.
7CI 5–10 · exposure 0 · augmentation 0 · importance 3.8/5 · click for rater detail
Repair broken scrolls by replacing them with new sections of tubing.
7| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Glass-blowing and finishing is a traditional craft sector with low digital adoption, small specialized firms, and heavy reliance on manual skill. Automation of precise glass work remains limited even in industrialized settings. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Glass blowing and scientific/artistic glasswork is a low-digitization, small-scale craft sector with essentially no AI or robotics adoption for physical fabrication tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 1/5 | AI offers minimal assistance to a craftsperson performing real-time glass repair and tubing replacement, since the task is fundamentally about tactile feedback, temperature judgment, and physical precision that current systems cannot augment meaningfully. |
| Augmentation potential | claude-sonnet-5 | 1/5 | AI offers no meaningful assistance for the physical manipulation of molten glass and tubing repair; this remains entirely a manual craft skill. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires precise manual manipulation of heated glass and custom-fitted tubing replacement in a physically complex environment. Current AI systems cannot perform end-to-end fabrication, heating, fitting, and installation of glass components at the necessary quality standard. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a hands-on physical craft task requiring manual dexterity, heat manipulation, and fine motor control that current AI systems cannot perform end-to-end; no software or robotic system can replace glass tubing sections autonomously today.' |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | This task involves substantial craft skill certification, liability for equipment damage and safety in a hazardous (high-temperature) environment, and strong organizational preference for human expertise. The specialized nature of the work and safety requirements create meaningful protection against automation. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing requirement exists, but the physical dexterity, specialized tools, and craft expertise needed create strong practical barriers to automation, though not legal ones. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | A skilled glass blower's manual labor is relatively inexpensive compared to the capital cost and integration overhead of a robotic system capable of safe glass heating, manipulation, and precision fitting in a production environment. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI or robotic substitute, so any hypothetical automation would require expensive custom robotics far exceeding the cost of a skilled glassworker's labor. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed product performs glass repair and scroll replacement reliably today. This task demands real-time sensorimotor control in a high-temperature workshop setting with custom geometry—well beyond current robotic or AI capabilities in production. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed products perform glass tube repair/replacement; this remains a purely manual craft skill with no robotic or AI system demonstrated in production for this niche task. |
Place rubber hoses on ends of tubing and charge tubing with gas.
7CI 5–10 · exposure 0 · augmentation 0 · importance 3.7/5 · click for rater detail
Place rubber hoses on ends of tubing and charge tubing with gas.
7| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Glassblowing and glassware manufacturing remain low-digitization, small-firm-dominated sectors with minimal AI or advanced robotics adoption; the craft nature of the work and small production volumes make automation investment unattractive. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Glass blowing and manual fabrication trades are a low-digitization, physical craft sector with minimal AI/robotics adoption to date. |
| Augmentation potential | claude-haiku-4-5-20251001 | 1/5 | Current AI offers no meaningful assistance for physically placing hoses on tubing or charging gas; this is a hands-on task where AI has no role in augmenting human performance even in a supportive capacity. |
| Augmentation potential | claude-sonnet-5 | 1/5 | Current AI tools offer no meaningful assistance for this hands-on physical assembly and gas-charging task. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task involves physical manipulation of rubber hoses and precise gas charging on glass tubing, requiring dexterous handling and spatial awareness in a three-dimensional workspace. Current AI systems lack the embodied robotics capabilities to reliably perform these fine motor operations at scale in a production environment. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical manipulation task requiring dexterity to fit hoses onto glass tubing and control gas charging, which current AI systems (software-based) cannot perform end-to-end without embodied robotics far beyond off-the-shelf capability.9 |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Glassblowing is a skilled craft with high technical training requirements and union presence in many shops; there are organizational and cultural barriers to automation, plus the task involves equipment requiring careful handling and safety protocols that create friction for robotic substitution. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing requirement exists, but safety concerns around gas handling and pressurized systems create meaningful organizational and safety-protocol barriers to automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Specialized robotic systems capable of handling delicate glass tubing and performing precise hose attachment and gas charging would be significantly more expensive to acquire, maintain, and integrate than paying skilled glassblowing workers to perform this task. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI-driven automation solution for this specific physical task, so any AI-based alternative (e.g., custom robotics) would be far more expensive than the human worker performing it. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed commercial AI systems or robots reliably perform the combined operation of fitting rubber hoses onto glass tubing ends and charging them with gas in a glassblowing context. This requires specialized physical dexterity and pressure/timing control not yet demonstrated in production settings. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product performs this specific physical glass-tubing task; it remains a manual craft/technical operation done by trained workers. |
Related occupations — Production
How to read this
A high substitution score does not mean this job disappears — it means a large share of its current tasks face replacement pressure, so the mix of tasks is likely to change. High augmentation alongside substitution typically means the occupation reorganizes around the protected tasks. Wide confidence intervals mean the rater panel disagreed: treat those scores as open questions, not verdicts.
What would change this score
New model capabilities (automatability, feasibility), falling inference costs (cost ratio), regulation and licensing shifts (barriers), and measured sector adoption (velocity) all re-enter at every index release. Each release is recomputed, versioned and kept queryable — scores are claims with a date on them, not permanent labels.